This workflow displays a variety of interactive Plotly visualizations to explore the human melanoma datas published in the recent Pozniak, et al 2022. Plotly visualizations found in this workflow are also found in the accompanying R Shiny App.
library("dplyr")
library("tibble")
library("ggplot2")
library("ggpubr")
library("GSVA")
library("readr")
library("heatmaply")
library("plotly")The aggregated Malignant cell dataset and test gene set
consisting of two gene files are loaded:
## read aggregated count human malignant dataset
df1 <- readRDS(file = "./counts/mal_agg_counts_fixed.rds")
# head(df1)This workflow is primarily designed with the intention to correlate two gene sets. Here we load such a data file with two toy gene sets. It is possible to upload a custom user-generated gene set as long as the file is a CSV or TSV file and the gene sets are the first and second columns. This workflow ignores additional columns, and these columns may be named whatever the end user wishes.
## read gene sets of interest to assay in the dataset
g1 <- readr::read_csv("./counts/test_genes.csv")
## display gene lists as data table
g1 %>%
DT::datatable()This block of code puts each row of the test gene set into a list
with each element in the list given the same name as the original
dataframe columns, however NA values are removed from the
list elements.
gl <- list()
for (i in 1:ncol(g1)) {
g1 %>%
dplyr::select(i) %>%
dplyr::pull() -> gl[[i]]
gl[[i]] <- gl[[i]][!is.na(gl[[i]])]
}
names(gl) <- names(g1)The app applies GSVA on the gene sets found in the test gene file. This is the most time consuming computational step in the app.
## run ssGSEA
GSVA::gsva(expr = as.matrix(df1 %>% dplyr::select(-gene) %>% as.data.frame()),
gset.idx.list = gl) -> gsva_sig## Estimating GSVA scores for 2 gene sets.
## Estimating ECDFs with Gaussian kernels
##
|
| | 0%
|
|=================================== | 50%
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|======================================================================| 100%The dataframe resulting from the GSVA step is converted to a long dataframe and the information contained in the aggregated cell names is used to re-label the aggregated cell types by Malignant_clusters, sample, GC number (patient) and BT/OT (time-point).
## convert data for violin Plot
gsva_sig %>%
as.data.frame() %>%
tibble::rownames_to_column() %>%
dplyr::rename(gene_sig = names(.)[1]) %>%
tidyr::gather(key = "cell_name", value = "expression", -gene_sig) %>%
dplyr::mutate(sample = gsub("^[^_]*.", "",
gsub("^[^_]*.", "",
gsub("_[^_]+$", "",
gsub("_[^_]+$", "", cell_name))))) %>%
dplyr::mutate(timepoint = dplyr::case_when(
grepl("BT", cell_name) ~ "BT",
grepl("OT", cell_name) ~ "OT")) %>%
dplyr::mutate(cell_type = dplyr::case_when(
grepl("Antigen", cell_name) ~ "Antigen Presentation",
grepl("Interferon", cell_name) ~ "Interferon Alpha Beta Response",
grepl("Melanocytic", cell_name) ~ "Melanocytic",
grepl("Mesenchymallike", cell_name) ~ "Mesenchymal like",
grepl("Mitochondrial", cell_name) ~ "Mitochondrial low quality",
grepl("Mitotic", cell_name) ~ "Mitotic",
grepl("NeuralCrestlike", cell_name) ~ "Neural Crest like",
grepl("PatientspecificA", cell_name) ~ "Patient specific A",
grepl("PatientspecificB", cell_name) ~ "Patient specific B",
grepl("Hypoxia", cell_name) ~ "Stress Hypoxia Response",
grepl("p53", cell_name) ~ "Stress p53 Response")) %>%
as.data.frame() -> a1
## define a vector of colors to match the aesthetics of the Pozniak 2022 paper
c("#1B9E77", "#A07125", "#B16548", "#8068AE", "#D03792", "#A66753", "#7FA718",
"#D9AA04", "#BF8B12", "#927132", "#666666") -> manual_color_scale This plot displays gene expression by cell type for each gene signature. Due to UI space constraints in the app, we will probably be limited to displaying only the first two gene sets.
a1 %>%
ggplot(aes(x = cell_type, y = expression, fill = cell_type)) +
geom_violin(alpha = 0.8) +
geom_point(position = position_jitter(seed = 1, width = 0.2),
size = 0.4, alpha = 0.8) +
theme_classic() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle("Violin plot of gene signature scores by cell type") +
scale_fill_manual(values = manual_color_scale) +
facet_grid(~gene_sig) -> p1
ggplotly(p1)# p1a1 %>%
ggplot(aes(x = timepoint, y = expression, fill = timepoint)) +
geom_violin(alpha = 0.8) +
geom_point(position = position_jitter(seed = 1, width = 0.2),
size = 0.4, alpha = 0.8) +
theme_classic() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle("Violin plot of gene signature scores by timepoint") +
facet_grid(rows = vars(gene_sig)) -> p2
ggplotly(p2)# p2a1 %>%
ggplot(aes(x = timepoint, y = expression, fill = cell_type)) +
geom_violin(alpha = 0.8) +
# geom_point(position = position_jitter(seed = 1, width = 0.2),
# size = 0.4, alpha = 0.8) +
theme_classic() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle("Violin plot of gene signature scores by cell type and timepoint") +
scale_fill_manual(values = manual_color_scale) +
facet_grid(rows = vars(gene_sig)) -> p3
ggplotly(p3)# p3This block of code selects all of the genes found in the text file, extracts the counts for each sample and averages them for these genes. The gene counts have been transformed to log scale in order for highly expressed genes not skew the gradient scale and drown out subtle changes in expression.
df1 %>%
dplyr::filter(gene %in% unlist(g1)[!is.na(unlist(g1))]) %>%
dplyr::select(gene, contains("OT"), contains("BT")) %>%
tidyr::gather(key = "cell_name", value = "expression", -gene) %>%
dplyr::mutate(sample = gsub("^[^_]*.", "",
gsub("^[^_]*.", "",
gsub("_[^_]+$", "",
gsub("_[^_]+$", "", cell_name))))) %>%
dplyr::mutate(timepoint = dplyr::case_when(
grepl("BT", cell_name) ~ "BT",
grepl("OT", cell_name) ~ "OT")) %>%
dplyr::mutate(cell_type = dplyr::case_when(
grepl("AntigenPresentation", cell_name) ~ "Antigen Presentation",
grepl("Interferon", cell_name) ~ "Interferon Alpha Beta Response",
grepl("Melanocytic", cell_name) ~ "Melanocytic",
grepl("Mesenchymallike", cell_name) ~ "Mesenchymal like",
grepl("Mitochondrial", cell_name) ~ "Mitochondrial low quality",
grepl("Mitotic", cell_name) ~ "Mitotic",
grepl("NeuralCrestlike", cell_name) ~ "Neural Crest like",
grepl("PatientspecificA", cell_name) ~ "Patient specific A",
grepl("PatientspecificB", cell_name) ~ "Patient specific B",
grepl("Hypoxia", cell_name) ~ "Stress Hypoxia Response",
grepl("p53", cell_name) ~ "Stress p53 Response")) %>%
dplyr::group_by(gene, cell_type) %>%
dplyr::summarise(mean_exp = mean(expression)) %>%
tidyr::spread(key = "cell_type", value = "mean_exp") %>%
tibble::column_to_rownames(var = "gene") %>%
dplyr::mutate_all(log) %>%
as.data.frame() -> r1
is.na(r1) <- sapply(r1, is.infinite)
r1[is.na(r1)] <- 0
heatmaply::heatmaply(
x = r1,
main = "Heatmap of log gene counts",
xlab = "Cell types",
ylab = "Genes of interest",
scale_fill_gradient_fun = ggplot2::scale_fill_gradient2(
low = "blue",
high = "firebrick"))df1 %>%
dplyr::filter(gene %in% unlist(g1)[!is.na(unlist(g1))]) %>%
dplyr::select(gene, contains("OT"), contains("BT")) %>%
tidyr::gather(key = "cell_name", value = "expression", -gene) %>%
dplyr::mutate(sample = gsub("^[^_]*.", "",
gsub("^[^_]*.", "",
gsub("_[^_]+$", "",
gsub("_[^_]+$", "", cell_name))))) %>%
dplyr::mutate(timepoint = dplyr::case_when(
grepl("BT", cell_name) ~ "BT",
grepl("OT", cell_name) ~ "OT")) %>%
dplyr::mutate(cell_type = dplyr::case_when(
grepl("AntigenPresentation", cell_name) ~ "Antigen Presentation",
grepl("Interferon", cell_name) ~ "Interferon Alpha Beta Response",
grepl("Melanocytic", cell_name) ~ "Melanocytic",
grepl("Mesenchymallike", cell_name) ~ "Mesenchymal like",
grepl("Mitochondrial", cell_name) ~ "Mitochondrial low quality",
grepl("Mitotic", cell_name) ~ "Mitotic",
grepl("NeuralCrestlike", cell_name) ~ "Neural Crest like",
grepl("PatientspecificA", cell_name) ~ "Patient specific A",
grepl("PatientspecificB", cell_name) ~ "Patient specific B",
grepl("Hypoxia", cell_name) ~ "Stress Hypoxia Response",
grepl("p53", cell_name) ~ "Stress p53 Response")) %>%
dplyr::mutate(cell_sample = paste0(cell_type, " ", timepoint)) %>%
dplyr::group_by(gene, cell_sample) %>%
dplyr::summarise(mean_exp = mean(expression)) %>%
tidyr::spread(key = "cell_sample", value = "mean_exp") %>%
tibble::column_to_rownames(var = "gene") %>%
dplyr::mutate_all(log) %>%
as.data.frame() -> r2
is.na(r2) <- sapply(r2, is.infinite)
r2[is.na(r2)] <- 0heatmaply::heatmaply(
x = r2,
main = "Heatmap of log gene counts",
xlab = "Cell types by time point",
ylab = "Genes of interest",
scale_fill_gradient_fun = ggplot2::scale_fill_gradient2(
low = "blue",
high = "firebrick"))r2 %>%
t() %>%
cor() %>%
heatmaply::heatmaply(
main = "Correlation Heatmap of log gene counts across samples",
xlab = "Cell types by time point",
ylab = "Genes of interest",
scale_fill_gradient_fun = ggplot2::scale_fill_gradient2(
low = "blue",
high = "firebrick"))r2 %>%
cor() %>%
heatmaply::heatmaply(
main = "Correlation Heatmap of log gene counts across samples",
xlab = "Cell types by time point",
ylab = "Genes of interest",
scale_fill_gradient_fun = ggplot2::scale_fill_gradient2(
low = "blue",
high = "firebrick"))The following correlation plots display how each gene set correlate each gene set with each other across cell types and time points. The correlation for each group is reported by displaying the R value and p-value.
gsva_sig %>%
t() %>% as.data.frame() %>%
tibble::rownames_to_column() %>%
dplyr::rename(sample_name = names(.)[1]) %>%
dplyr::mutate(timepoint = dplyr::case_when(
grepl("BT", sample_name) ~ "BT",
grepl("OT", sample_name) ~ "OT")) %>%
dplyr::mutate(cell_type = dplyr::case_when(
grepl("AntigenPresentation", sample_name) ~ "Antigen Presentation",
grepl("Interferon", sample_name) ~ "Interferon Alpha Beta Response",
grepl("Melanocytic", sample_name) ~ "Melanocytic",
grepl("Mesenchymallike", sample_name) ~ "Mesenchymal like",
grepl("Mitochondrial", sample_name) ~ "Mitochondrial low quality",
grepl("Mitotic", sample_name) ~ "Mitotic",
grepl("NeuralCrestlike", sample_name) ~ "Neural Crest like",
grepl("PatientspecificA", sample_name) ~ "Patient specific A",
grepl("PatientspecificB", sample_name) ~ "Patient specific B",
grepl("Hypoxia", sample_name) ~ "Stress Hypoxia Response",
grepl("p53", sample_name) ~ "Stress p53 Response")) %>%
dplyr::mutate(stem_name = paste0(cell_type, " ", timepoint)) %>%
as.data.frame() -> d1d1 %>%
dplyr::mutate(sample = gsub("^[^_]*.", "",
gsub("^[^_]*.", "",
gsub("_[^_]+$", "",
gsub("_[^_]+$", "", sample_name))))) %>%
dplyr::group_by(sample) %>%
dplyr::summarise(mean_gene1 = mean(gene1),
mean_gene2 = mean(gene2)) %>%
ggplot(aes(x = mean_gene1, y = mean_gene2)) +
geom_point(size = 1, color = "black") +
stat_smooth(method = "lm", se = TRUE, fill = "gray", color = "darkgray",
formula = y ~ poly(x, 1, raw = TRUE)) +
ggpubr::stat_cor(method = "spearman", label.x = 0) +
theme_classic() +
xlab("Signature Score 1") +
ylab("Signature Score 2") +
theme(axis.title = element_text(size = 14),
legend.position = "none") +
ggtitle(paste0("Correlation between signature scores across cell types")) -> p0
ggplotly(p0)d1 %>%
dplyr::filter(!grepl("Patient", cell_type)) %>%
ggplot(aes(x = gene1, y = gene2)) +
geom_point(size = 1, aes(color = stem_name)) +
stat_smooth(method = "lm", se = TRUE, fill = "gray", color = "darkgray",
formula = y ~ poly(x, 1, raw = TRUE)) +
ggpubr::stat_cor(method = "spearman", label.x = 0) +
theme_classic() +
xlab("Signature Score 1") +
ylab("Signature Score 2") +
theme(axis.title = element_text(size = 14),
legend.position = "none") +
ggtitle(paste0("Correlation between signature scores across cell types")) +
facet_grid(cols = vars(cell_type)) -> p4
# p4
ggplotly(p4)d1 %>%
dplyr::filter(!grepl("Patient", cell_type)) %>%
ggplot(aes(x = gene1, y = gene2)) +
geom_point(size = 1, color = "black") +
stat_smooth(method = "lm", se = TRUE, fill = "gray", color = "darkgray",
formula = y ~ poly(x, 1, raw = TRUE)) +
ggpubr::stat_cor(method = "spearman", label.x = 0) +
theme_classic() +
xlab("Signature Score 1") +
ylab("Signature Score 2") +
theme(axis.title = element_text(size = 14),
legend.position = "none") +
ggtitle(paste0("Correlation between signature scores across timepoints")) +
facet_grid(rows = vars(timepoint)) -> p5
# p5
ggplotly(p5)d1 %>%
dplyr::filter(!grepl("Patient", cell_type)) %>%
ggplot(aes(x = gene1, y = gene2)) +
geom_point(size = 1, aes(color = stem_name)) +
stat_smooth(method = "lm", se = TRUE, fill = "gray", color = "darkgray",
formula = y ~ poly(x, 1, raw = TRUE)) +
ggpubr::stat_cor(method = "spearman", label.x = 0) +
theme_classic() +
xlab("Signature Score 1") +
ylab("Signature Score 2") +
theme(axis.title = element_text(size = 14),
legend.position = "none") +
ggtitle(paste0("Correlation between signature scores across cell types",
"and timepoints")) +
facet_grid(rows = vars(timepoint),
cols = vars(cell_type)) -> p6
# p6
ggplotly(p6)sessioninfo::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.2 (2022-10-31)
## os macOS Big Sur ... 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Europe/Brussels
## date 2022-12-15
## pandoc 2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
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## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.1)
## TSP 1.2-1 2022-07-14 [1] CRAN (R 4.2.0)
## tzdb 0.3.0 2022-03-28 [1] CRAN (R 4.2.0)
## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0)
## vctrs 0.5.1 2022-11-16 [1] CRAN (R 4.2.2)
## viridis * 0.6.2 2021-10-13 [1] CRAN (R 4.2.0)
## viridisLite * 0.4.1 2022-08-22 [1] CRAN (R 4.2.0)
## vroom 1.6.0 2022-09-30 [1] CRAN (R 4.2.0)
## webshot 0.5.4 2022-09-26 [1] CRAN (R 4.2.1)
## withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
## xfun 0.35 2022-11-16 [1] CRAN (R 4.2.2)
## XML 3.99-0.13 2022-12-04 [1] CRAN (R 4.2.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
## XVector 0.36.0 2022-04-26 [1] Bioconductor
## yaml 2.3.6 2022-10-18 [1] CRAN (R 4.2.1)
## zlibbioc 1.42.0 2022-04-26 [1] Bioconductor
##
## [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
##
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